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Aggregate power flexibility of multi-energy systems supported by dynamic networks

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  • Li, Hengyi
  • Qin, Boyu
  • Wang, Shihan
  • Ding, Tao
  • Liu, Jialing
  • Wang, Hongzhen

Abstract

The multi-energy system, encompassing electricity networks, district heating networks (DHNs), and hydrogen-enriched compressed natural gas (HCNG) networks, provides an alternative for promoting intermittent renewable energy accommodation and enhancing operational flexibility. This paper investigates the aggregate flexibility of the multi-energy system based on dynamic network models and admissible power fluctuation regions of decomposed subsystems. The dynamic processes within HCNG networks/DHNs are analyzed, and the inner-box method is employed to approximately quantify the individual flexibility of the dynamic networks. Furthermore, the optimal dispatchable regions of decomposed subsystems, accounting for internal gas/heating load uncertainties, are evaluated based on distributionally robust optimization. Through distribution-level power aggregation, the flexibility of the multi-energy system is quantified utilizing geometric methods. Numerical results on two test systems verify the effectiveness of the proposed methodology.

Suggested Citation

  • Li, Hengyi & Qin, Boyu & Wang, Shihan & Ding, Tao & Liu, Jialing & Wang, Hongzhen, 2025. "Aggregate power flexibility of multi-energy systems supported by dynamic networks," Applied Energy, Elsevier, vol. 377(PB).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pb:s0306261924019482
    DOI: 10.1016/j.apenergy.2024.124565
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    References listed on IDEAS

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    1. Zhao, Yongning & Xu, Xiandong & Qadrdan, Meysam & Wu, Jianzhong, 2021. "Optimal operation of compressor units in gas networks to provide flexibility to power systems," Applied Energy, Elsevier, vol. 290(C).
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    3. Tian, Jinfang & Yu, Longguang & Xue, Rui & Zhuang, Shan & Shan, Yuli, 2022. "Global low-carbon energy transition in the post-COVID-19 era," Applied Energy, Elsevier, vol. 307(C).
    4. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Gao, Chong, 2023. "Multi-stage distributionally robust optimization for hybrid energy storage in regional integrated energy system considering robustness and nonanticipativity," Energy, Elsevier, vol. 277(C).
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